Green electricity control parameter combination optimization method and system oriented to user demand response and storage medium

Abstract

The invention discloses a user demand response-oriented green electricity control parameter combination optimization method and system and a storage medium, and belongs to the technical field of electric power energy. Relates to the technical field of distribution automation. The method comprises the following steps: acquiring green electricity consumption data, electricity price information and constraint conditions of a user side, and constructing a demand decision model of a user under different prices and constraints to obtain a user decision relation set; based on the relational expression set, deducing optimal decisions of the user under different control parameters to obtain a user reaction function; embedding the user reaction function as a subordinate layer equilibrium constraint, and constructing a Stackelberg double-layer optimization model by taking the comprehensive performance as an upper objective function; and solving the double-layer optimization model to obtain an optimal control parameter. The method is used for solving the problems that existing green electricity control parameter setting depends on experience, differentiation responses of users are difficult to describe, and efficiency and fairness are difficult to consider at the same time.

Description

Technical Field

[0001] This invention relates to the field of power energy technology, and more specifically, to a method, system, and storage medium for optimizing the combination of green electricity control parameters in response to user demand. Background Technology

[0002] Guiding green electricity consumption and setting policy parameters (such as subsidy intensity, quota ratio, and carbon price transmission) are key steps in promoting carbon reduction on the end-user side of energy consumption. Current practices typically rely on empirical rules, local pilot programs, or static elasticity estimations to set parameters, or employ a partial equilibrium analysis framework that treats price as an exogenous factor. These methods struggle to systematically characterize the heterogeneous preferences and constraints of different user types (industrial, commercial, and residential), and fail to accurately reflect the interconnected impact of policy tools such as prices, subsidies, and quotas on micro-level decisions. This results in highly subjective parameter settings, weak verifiability, and significant fluctuations in policy effects across different user groups and time periods.

[0003] To address these shortcomings, research and practice have attempted to introduce demand response and market clearing analysis, but these approaches generally suffer from three limitations: First, user-side behavior is poorly characterized, often using a single average price elasticity to substitute for the objective functions and constraints of different users, neglecting key differences such as compliance / carbon emission thresholds on the industrial side, brand / ESG utility items on the commercial side, and environmental preferences and achievable limits on the residential side; second, price and certificate (or green certificate) mechanisms are often simply exogenized, failing to incorporate the endogenous relationship between certificate prices and total purchase volume, thus failing to predict the chain feedback of "others' purchasing behavior—certificate price changes—this user's optimal decision"; and third, there is a lack of a unified optimization framework that can connect the optimal response at the user level with the overall performance objectives at the higher level, making it difficult to make calculable and reproducible trade-offs between efficiency, cost, and emission reduction benefits.

[0004] Existing parameter calibration and effect evaluation still face challenges in both data and computation. On the data front, multi-source electricity consumption and price data are scattered across different channels such as electricity prices, certificate prices, carbon prices, and subsidy records, with inconsistent time granularity and scope. Directly using this data for user decision-making modeling and comparative static analysis can easily lead to biases. On the computational front, if policy design does not explicitly link user optimality conditions with market clearing relationships, it often relies on extensive scenario simulations or manual parameter tuning, lacking a unified objective function and constraint expression, making it difficult to form interpretable and verifiable optimal parameter combinations. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a green electricity control parameter combination optimization method oriented towards user demand response. By constructing a categorized demand decision model, deriving the user response function, and embedding it as a subordinate layer equilibrium constraint into a Stackelberg two-layer optimization model with comprehensive performance as the objective, the present invention addresses the problems of existing green electricity policy parameter settings relying on experience, difficulty in characterizing differentiated user responses, and difficulty in balancing efficiency and fairness.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A method for optimizing the combination of green electricity control parameters for user demand response includes the following steps: acquiring green electricity consumption data, electricity price information, and constraints on the user side, and constructing a user demand decision model under different prices and constraints to obtain a set of user decision relations; based on the set of relations, deriving the optimal decision of the user under different control parameters to obtain the user reaction function; embedding the user reaction function as a subordinate layer equilibrium constraint, and constructing a Stackelberg two-layer optimization model with comprehensive performance as the upper-layer objective function; solving the two-layer optimization model to obtain the optimal control parameters.

[0007] In a preferred embodiment, the subordinate layer equilibrium constraint further includes the endogenous decomposition of the terminal settlement price and the first-order optimality condition of the internal solution.

[0008] In a preferred embodiment, the specific steps for constructing the demand decision model are as follows: Users are categorized into industrial, commercial, and residential types; annual electricity consumption, electricity price information, and constraints for each type are obtained to establish corresponding input datasets; based on the input datasets, demand decision criteria for different user types are established, with a cost minimization criterion set for industrial users, a utility maximization criterion set for commercial users, and an environmental preference and expenditure minimization criterion set for residential users; and based on the demand decision criteria, combined with the user-side price system and constraints, a demand decision model for each user type is constructed.

[0009] In a preferred embodiment, the set of user decision relations specifically refers to: obtaining a set of user decision relations related to control parameters by solving the demand decision model for each type of user and utilizing the optimality condition.

[0010] In a preferred embodiment, the user reaction function is specifically: by substituting the optimality conditions in the user decision relation set into specific control parameters and solving the optimality conditions, the optimal decision quantity of each type of user under different control parameters is obtained as the user reaction function.

[0011] In a preferred embodiment, the upper-level objective function is specifically as follows:

[0012] In the formula, For subsidy vectors, For quota vectors, For carbon tax rate, For users' equilibrium strategy combinations, For (certificates / green electricity) against demand, For the marginal cost of technology, For environmental damage function, For the shadow price of public funds, This refers to the unit green electricity subsidy for user i, where i is the user type index. This is the user response function.

[0013] In a preferred embodiment, the Stackelberg two-level optimization model is solved using backward induction, with the following specific steps: given control parameters, solve for the user reaction function; substitute the user reaction function into the upper-level objective function to obtain the reduced form of the optimization problem; apply the first-order conditions to the optimization problem to obtain the optimal control parameters.

[0014] In a preferred embodiment, the optimal control parameters must satisfy the first-order necessary condition for the upper-level objective function to be optimal at the interior point.

[0015] This invention provides a green electricity control parameter combination optimization system oriented towards user demand response, comprising: a user demand modeling module, used to acquire green electricity consumption data, electricity price information and constraints on the user side, and construct a user demand decision model under different prices and constraints to obtain a set of user decision relational expressions; a user response modeling module, used to derive the optimal decision of the user under different control parameters based on the set of relational expressions to obtain the user response function; an optimization model construction module, used to embed the user response function as a subordinate layer equilibrium constraint and construct a Stackelberg two-layer optimization model with comprehensive performance as the upper-layer objective function; and a model solving module, used to solve the two-layer optimization model to obtain the optimal control parameters.

[0016] A readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a remote monitoring method for continuous metering with live replacement of meters.

[0017] The technical effects and advantages of the multi-source load response intelligent distribution network hierarchical collaborative control method of this invention are as follows: This invention constructs a categorized demand decision-making model based on user-side electricity consumption and price constraint data, derives user response functions, and embeds them as subordinate-level equilibrium constraints into a Stackelberg bi-level optimization model with comprehensive performance as the objective. This enables data-driven determination of optimal control parameters while consistently characterizing heterogeneous user responses and market interactions. Consequently, it achieves objectivity and verifiability in policy parameter setting, significantly improves the efficiency and predictability of green electricity allocation and guidance, reduces welfare losses caused by arbitrary parameter settings and execution deviations, and enhances overall system performance and green electricity consumption levels under given constraints. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the green electricity control parameter combination optimization method for responding to user needs provided in an embodiment of the present invention; Figure 2 This is a block diagram of a green electricity control parameter combination optimization system for responding to user needs, provided in an embodiment of the present invention. Figure 3 This is a schematic diagram of an exemplary storage medium that can be used to implement embodiments of the present disclosure, as provided in the embodiments of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1: The symbols and their meanings involved in this example are summarized in Table 1 below: Table 1

[0021] Figure 1 This invention presents a method for optimizing the combination of green electricity control parameters in response to user needs, comprising the following steps: S1: Obtain green electricity consumption data, electricity price information and constraints from the user side, and construct a demand decision model for users under different prices and constraints to obtain a set of user decision relationship formulas.

[0022] In this embodiment, the specific steps for constructing the demand decision model are as follows: S101, classify users into industrial, commercial and residential categories, obtain the annual electricity consumption scale, electricity price information and constraints of each type, and establish the corresponding input dataset; S102, Based on the input dataset, establish demand decision criteria for different types of users, set a cost minimization criterion for industrial users, a utility maximization criterion for commercial users, and a environmental preference and expenditure minimization criterion for residential users; S103. Based on the aforementioned demand decision criteria, and combined with the user-side pricing system and constraints, construct a demand decision model for each type of user.

[0023] The above steps are as follows: First, users need to be categorized: industrial users, commercial users, and residential users. Each user type has different green electricity consumption scales, electricity price information, and constraints. Industrial users typically face large electricity demands and focus on cost control; commercial users, when making green electricity consumption decisions, consider brand value and social responsibility (ESG) in addition to economic factors; residential users make decisions based on maximizing utility, considering non-economic factors such as environmental awareness, budget constraints, and social responsibility. For industrial users, the decision-making criterion is cost minimization, that is, to choose the optimal amount of green electricity to purchase under a given electricity demand in order to minimize costs; the decision-making criterion for commercial users is utility maximization, which not only considers the cost of electricity purchase, but also the brand benefits and social responsibility brought about by using green electricity; while residential users make decisions based on environmental preferences and expenditure minimization criteria, focusing on the social benefits of green electricity consumption and its contribution to environmental protection. The decision-making model for each user type is constructed based on the aforementioned criteria and constraints. The demand decision-making model for industrial users is solved by minimizing costs, taking into account factors such as their electricity demand, the price of green electricity, the price of conventional electricity, and the price of carbon emission rights. Therefore, the expression for the demand decision-making model for industrial users is as follows: The objective function is:

[0024] in, This represents the "effective cost" or "opportunity cost" of fossil fuels. In this model, The value depends on Does this lead to tighter carbon quota constraints?

[0025] It should be noted that utility functions are used to unify the representation of variables. The preference structure reflecting user i is the negative value of the cost function for industrial users. For commercial and residential users, the aforementioned utility function form is directly adopted; The constraints are:

[0026] The third carbon emission quota constraint stipulates the user's use of fossil fuels. The carbon emissions generated must not exceed their annual quota. .

[0027] The demand decision model for commercial users incorporates the utility maximization principle, including electricity procurement costs, brand effect, and the social responsibility requirements of using green electricity. Its demand decision model expression is as follows:

[0028] In the formula, The revenue function representing commercial users can be considered, in the short term, as a function related to total electricity consumption but with a weaker relationship to the proportion of green electricity. The total cost of electricity procurement, including expenditures on both green and conventional electricity purchases; Used to characterize the brand value and reputation effect brought about by the use of green electricity, the logarithmic function form reflects the diminishing marginal utility of brand utility, that is, the use of green electricity can significantly enhance the corporate image in the early stage, but as the amount of green electricity used increases, the marginal brand effect gradually weakens. Business users' decisions are subject to the following constraints:

[0029] The demand decision model for residential users considers environmental preferences and expenditure minimization criteria, and describes the demand for green electricity consumption by residents through the following utility function:

[0030] in, And assume The extremely low transmission coefficient, given exogenously, reflects the strong regulation of residential electricity prices. Indicates residents' total electricity consumption The basic utility gained from this is unrelated to green electricity choices and reflects the satisfaction of basic electricity consumption needs. Electricity expenses for residents reflect the impact of budget constraints on consumption decisions; The square root function represents the sense of environmental satisfaction and social responsibility that residents gain from green electricity consumption. It reflects the law of diminishing marginal utility, which is consistent with the principle of diminishing marginal utility in psychology. That is, as green electricity consumption increases, the psychological satisfaction brought by an additional unit of green electricity gradually decreases. Unlike industrial and commercial users, residential users are typically not subject to mandatory green electricity quotas or direct carbon emission constraints. Their decisions primarily involve a trade-off between price sensitivity and environmental preferences. In practice, residential green electricity consumption may also be constrained by factors such as supply accessibility, information awareness, and technological conditions, as expressed mathematically below:

[0031] in The upper limit for residential green electricity purchases is determined by market or technological conditions.

[0032] The decision-making models of the three types of users described above collectively constitute a complete picture of the demand side of the green electricity market, providing a theoretical basis for analyzing the effects of different policy tools. By solving the optimization problems of each type of user, the green electricity demand response function can be obtained, and then the impact of changes in policy parameters such as carbon prices, subsidy levels, and quota requirements on market equilibrium can be analyzed.

[0033] Furthermore, the set of user decision-making relationships specifically refers to: by solving the demand decision model for each type of user and utilizing the optimality condition, a set of user decision-making relationships related to the control parameters is obtained.

[0034] Specifically: (1) First, the price system and control / exogenous parameters in this embodiment are described in a unified manner: The end-user green electricity price is based on the electricity price. With certificate price Endogenous decomposition, its expression is as follows:

[0035] The greater the total amount of green electricity purchased, the higher the equilibrium certificate price. The effective price of traditional fossil fuel electricity is denoted as:

[0036] (2) User decision-making relationship of industrial users: Under the cost minimization model, the net price difference formula is defined as follows:

[0037] Combining quotas and carbon emission constraints, the lower bound for decision-making is defined as:

[0038] The set of decision relations corresponding to the optimality conditions is divided into the following segmentation rules:

[0039] The above three criteria constitute the decision-making relationship for industrial users. This formula shows that when the net cost of green electricity is lower than that of fossil electricity, industrial users tend to use all green electricity; when the net cost is higher, they only purchase the minimum amount of green electricity that meets the quota and carbon constraints; when the costs of the two are comparable, they are in the indifference range.

[0040] (3) User decision-making relationship of commercial users: To solve the demand decision model for business users, the first necessary condition for the internal solution is:

[0041] Taking this as the optimal condition for business users' demand decisions, the decision relationship is as follows:

[0042] And tailor it in conjunction with the following feasible domains and quotas: ,

[0043] (4) User decision-making relationship of residential users: Solving the demand decision model for residential users, the first necessary condition for the internal solution is:

[0044] Taking this as the optimal condition for residential user demand decisions, the decision relationship is as follows:

[0045] And tailoring based on the following residents' accessibility / supply caps:

[0046] (5) Unified optimal response relationship when certificate price is endogenous: When using Given the intrinsic certificate price setting and the existence of an internal solution, the unified optimality condition expression for various users under given control parameters is as follows:

[0047] The above formula is used to characterize the strategy substitutability of "others' usage -> certificate price change -> my own optimal strategy"; together with the boundary / first-order conditions of various users in (2)-(4), it constitutes a set of user decision relation formulas.

[0048] It should be noted that (2)-(5) above is the formalized expansion of the user decision relation set; where the control parameters include, but are not limited to, the following: and through The interaction mechanism among lower-level users is made explicit through the interaction of prices.

[0049] S2, based on the set of relations, derive the user's optimal decision under different control parameters to obtain the user's response function.

[0050] In this embodiment, the user reaction function is specifically defined as follows: by substituting the optimality conditions in the user decision relation set into specific control parameters and solving the optimality conditions, the optimal decision quantity of each type of user under different control parameters is obtained as the user reaction function.

[0051] Specifically: (1) Industrial user reaction function In the case of industrial users, step S1 has already given the first-order / boundary optimality conditions of the cost minimization model. Boundary judgment can determine the optimality of the industrial user given control parameters. The optimal green electricity purchase amount, i.e., the industrial user response function, is:

[0052] When it appears When the interval is indifferent, a deterministic value selection rule can be selected according to implementation requirements, for example, taking... Conservative rules, or take The saturation rule is maintained and kept consistent in numerical implementation; when the carbon constraint becomes a tight constraint, the above equation degenerates into:

[0053] (2) Business User Reaction Function In the case of commercial users, step S1 has already given the first-order necessary conditions for the internal solution. Given the control parameters... Solving for this condition yields the internal optimal decision quantity for the business user, as shown in the following equation:

[0054] The business user reaction function, obtained by combining the constraints, is shown in the following equation:

[0055] This shows that green electricity has a relative premium. The rise and fall And preference parameters rise ;when or When the smaller value causes the internal solution to exceed the upper bound, Automatic saturation .

[0056] (3) Residential User Response Function In the case of residential users, step S1 has already given the first-order necessary conditions for the internal solution. Given the control parameters... Solving for this condition yields the internal optimal decision quantity for the residential user, as shown in the following equation:

[0057] Combining the upper bound constraint and the nonnegativity constraint, the resident user reaction function is:

[0058] Therefore, The increase decreases in an inverse square manner. ,and The increase is a squared increase ;when Too small or When it is large enough that the internal solution exceeds the upper bound, Saturated .

[0059] (4) Implicit User Reaction Function in the Case of Endogenous Certificate Price In the case of endogenous certificate price, the terminal price This depends on the total amount of green electricity purchased by all users. At this point, the optimality condition for each user must be explicitly factored into the price feedback term. The internal solution satisfies the unified optimality condition expression, and the user reaction function defined therefrom is the optimal response mapping with respect to the decision vectors of others, as shown in the following equation:

[0060] and price closed-loop conditions Solving the system of equations simultaneously yields the subordinate layer equilibrium, where, Let be the green electricity decision vector for all users except user i. Under the condition that... And various demands regarding Under the common setting of monotonically decreasing, the simultaneous system has a unique fixed point solution, which can be used as a subordinate layer equilibrium constraint for subsequent optimization of higher-level objectives.

[0061] The above three types of users, under the condition of "given price", receive And in the case of "endogenous price" Together with the price loop, the result of the user reaction function in this step is used in step S3 to embed the upper-level objective function for Stackelberg bi-level optimization.

[0062] S3 embeds the user reaction function as a subordinate layer equilibrium constraint and constructs a Stackelberg two-layer optimization model with the comprehensive performance as the upper-layer objective function.

[0063] In this embodiment, the upper-level objective function is specifically as follows:

[0064] Where W represents social welfare, , For (certificates / green electricity) against demand, For the marginal cost of technology, For environmental damage function, The shadow price of public funds (if fiscal distortions are ignored, then take...) Carbon price revenue and subsidies are both transfer payments and do not enter net welfare unless fiscal distortions are accounted for.

[0065] The subordinate layer equilibrium constraint is jointly given by the user reaction function obtained in step S2 and the market clearing relationship. Firstly, in the case of "exogenous price", the explicit / piecewise reaction functions of various types of users are directly adopted:

[0066] And combined with feasible region constraints , and resident accessibility constraints .

[0067] Secondly, in the case of "endogenous certificate price," the final settlement price is based on... Decomposition, and the certificate price satisfies The monotonic relation; the internal solutions of various users must satisfy a unified first-order necessary condition, and together with the feasible region constraints, constitute the optimal response mapping. ; Furthermore, in conjunction with market clearing constraints:

[0068] Thus, given at the upper level At that time, subordinate layer solution and Together, we determine a unique equilibrium point.

[0069] In summary, the upper level selects... maximize The lower layer provides an equilibrium through the relationship between user reaction functions and market clearing. and Together, they constitute the Stackelberg two-layer optimization model in this embodiment.

[0070] S4. Solve the two-layer optimization model to obtain the optimal control parameters.

[0071] In this embodiment, the Stackelberg two-level optimization model is solved using backward induction, and the specific steps are as follows: S401, Given control parameters, solve for the user response function. Specifically, given control parameters... Solving the user's optimization problem yields the reaction function. For the internal solution, the first-order condition is:

[0072] in Let be the reaction function value for user i, which characterizes how their optimal strategy changes with policy parameters. By comparing static analyses, the direction and magnitude of the policy effect can be obtained, for example... >0 indicates that increased subsidies will promote the consumption of green electricity.

[0073] S402, user response function Substituting into the upper-level objective function, we obtain the optimization problem in its reduced form:

[0074] S403, Solve the first-order conditions for the optimization problem to obtain the optimal control parameters. The first necessary condition for the upper-level objective function to be optimal at the interior points should be satisfied, and the specific formula is as follows:

[0075]

[0076] The above conditions reflect the basic principle of control strategy formulation: the marginal cost of the strategy (such as fiscal expenditure) should equal the marginal benefit (such as environmental improvement and increased economic efficiency). (First item) The second item reflects the direct fiscal cost of subsidies. This demonstrates the indirect welfare effect of subsidies by changing user behavior.

[0077] Example 2, Figure 2 A green electricity control parameter combination optimization system oriented towards user demand response is presented, including: The user demand modeling module is used to acquire green electricity consumption data, electricity price information and constraints from the user side, and to build a user demand decision model under different prices and constraints, thereby obtaining a set of user decision relationship formulas. The user response modeling module is used to derive the user's optimal decision under different control parameters based on the set of relations, and obtain the user response function; The optimization model building module is used to embed the user reaction function as a subordinate layer equilibrium constraint and construct a Stackelberg two-layer optimization model with the comprehensive performance as the upper layer objective function. The model solving module is used to solve the two-layer optimization model to obtain the optimal control parameters.

[0078] Example 3, A readable storage medium having a computer program stored thereon, such as Figure 3 As shown, when the computer program is executed by the processor, it implements any of the embodiments in Example 1.

[0079] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0080] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0081] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0082] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0083] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0084] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimizing the combination of green electricity control parameters in response to user needs, characterized in that, Includes the following steps: Obtain green electricity consumption data, electricity price information and constraints from the user side, and construct a demand decision model for users under different prices and constraints to obtain a set of user decision relationship formulas; Based on the set of relationships, the optimal decision of the user under different control parameters is derived, and the user response function is obtained. The user reaction function is embedded as a subordinate layer equilibrium constraint, and the Stackelberg two-layer optimization model is constructed with the comprehensive performance as the upper layer objective function. Solve the two-layer optimization model to obtain the optimal control parameters.

2. The green electricity control parameter combination optimization method oriented towards user demand response according to claim 1, characterized in that, The subordinate layer equilibrium constraint also includes the endogenous decomposition of the terminal settlement price and the first-order optimality condition of the internal solution.

3. The green electricity control parameter combination optimization method oriented towards user demand response according to claim 1, characterized in that, The specific steps for constructing the demand decision model are as follows: The user types are divided into industrial, commercial and residential categories. The annual electricity consumption scale, electricity price information and constraints of each type are obtained, and the corresponding input dataset is established. Based on the input dataset, demand decision criteria for different types of users are established: a cost minimization criterion is set for industrial users, a utility maximization criterion is set for commercial users, and a criterion for environmental preference and expenditure minimization is set for residential users. Based on the aforementioned demand decision criteria, and combined with the pricing system and constraints on the user side, a demand decision model for each type of user is constructed.

4. The green electricity control parameter combination optimization method oriented towards user demand response according to claim 3, characterized in that, The user decision relation set is specifically obtained by solving the demand decision model for each type of user and using the optimality condition to obtain the user decision relation set related to the control parameters.

5. The green electricity control parameter combination optimization method oriented towards user demand response according to claim 4, characterized in that, The user reaction function is specifically defined as follows: by substituting the optimality conditions in the user decision relation set into specific control parameters and solving the optimality conditions, the optimal decision quantity of each type of user under different control parameters is obtained as the user reaction function.

6. The green electricity control parameter combination optimization method oriented towards user demand response according to claim 5, characterized in that, The specific objective function of the upper layer is as follows: In the formula, For subsidy vectors, For quota vectors, For carbon tax rate, For the user's equilibrium strategy combination, For (certificates / green electricity) against demand, For the marginal cost of technology, For environmental damage function, For the shadow price of public funds, This refers to the unit green electricity subsidy for user i, where i is the user type index. This is the user response function.

7. The green electricity control parameter combination optimization method for user demand response according to claim 6, characterized in that, The Stackelberg two-level optimization model is solved using backward induction, and the specific steps are as follows: Given control parameters, solve for the user response function; Substituting the user response function into the upper objective function yields a reduced form of the optimization problem. The first-order conditions for the optimization problem are obtained to obtain the optimal control parameters.

8. The green electricity control parameter combination optimization method for user demand response according to claim 7, characterized in that, The optimal control parameters must satisfy the first-order necessary condition for the upper-level objective function to be optimal at the interior point.

9. A system using the user-demand-responsive green electricity control parameter combination optimization method as described in any one of claims 1-8, characterized in that, include: The user demand modeling module is used to acquire green electricity consumption data, electricity price information and constraints from the user side, and to build a user demand decision model under different prices and constraints, thereby obtaining a set of user decision relationship formulas. The user response modeling module is used to derive the user's optimal decision under different control parameters based on the set of relations, and obtain the user response function; The optimization model building module is used to embed the user reaction function as a subordinate layer equilibrium constraint and construct a Stackelberg two-layer optimization model with the comprehensive performance as the upper layer objective function. The model solving module is used to solve the two-layer optimization model to obtain the optimal control parameters.

10. A readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements each step of the green electricity control parameter combination optimization method for user demand response as described in any one of claims 1-8.

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